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Advances in Decision Sciences (ADS)

Advances in Decision Sciences (ADS)

Published by Asia University, Taiwan; Scientific and Business World

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Predicting Efficiency of Commercial Banks in Vietnam: A DEA and Machine Learning Approach

Predicting Efficiency of Commercial Banks in Vietnam: A DEA and Machine Learning Approach

Title

Predicting Efficiency of Commercial Banks in Vietnam: A DEA and Machine Learning Approach

Authors

  • Quang Hung Do
    Posts and Telecommunications Institute of Technology, Hanoi, Vietnam

Abstract

Purpose: This study investigates the effectiveness of a hybrid Data Envelopment Analysis (DEA) and Machine Learning (ML) approach in predicting the efficiency of commercial banks in Vietnam.
Methodology: A two-stage model is proposed. First, DEA is employed to evaluate bank efficiency from 2012 to 2021 using data from annual reports. Second, various ML algorithms (ANN-MLP, linear regression, random forest) are used to forecast efficiency scores based on the DEA results. The performance of each ML model is compared to identify the most effective approach.
Findings: The findings suggest that the ANN-MLP model outperforms other ML methods in predicting bank efficiency.
Research limitations/implications: The study utilizes data from a specific timeframe and may be limited by potential inaccuracies in financial statements. Future research could extend the time period and explore additional data sources.
Practical implications: The proposed DEA-ML model can be a valuable tool for bank managers and policymakers to assess and predict bank efficiency, ultimately leading to improved decision-making, greater efficiency, and enhanced competitiveness. The findings might be generalizable to other bank types in similar contexts.
Social implications: This research contributes to the development of DEA-ML models, potentially influencing practices in bank efficiency measurement and leading to a more robust financial system.
Originality/value: This study offers a novel approach for combining DEA and ML techniques to predict commercial bank efficiency in Vietnam. The findings demonstrate the potential of ANN-MLP for financial applications and provide valuable insights for bank management and financial regulation.

Keywords

Vietnamese commercial banks; Efficiency; Data Envelopment Analysis; Machine learning.

Classification-JEL

G21, C14, C53

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ISSN 2090-3359 (Print)
ISSN 2090-3367 (Online)

Asia University, Taiwan

Scientific and Business World

4.7
2023CiteScore
 
86th percentile
Powered by  Scopus
SCImago Journal & Country Rank
Q2 in Scopus
CiteScore 2023 = 4.7
CiteScoreTracker 2024 = 8.5
SNIP 2023 = 0.799
SJR Quartile = Q1
SJR 2024 = 0.814
H-Index = 20

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